title: Heart rate variability dynamics for the prognosis of cardiovascular risk.
abstract: Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis.
authors.affiliation: Computational Neuroscience, Department of Physics, Universidad Autonoma de Occidente, Cali, Colombia. juanfelipe.rv@gmail.com
authors.forename: Juan F
authors.initials: JF
authors.lastname: Ramirez-Villegas
authors.forename: Eric
authors.initials: E
authors.lastname: Lam-Espinosa
authors.forename: David F
authors.initials: DF
authors.lastname: Ramirez-Moreno
authors.forename: Paulo C
authors.initials: PC
authors.lastname: Calvo-Echeverry
authors.forename: Wilfredo
authors.initials: W
authors.lastname: Agredo-Rodriguez
mesh_terms: D000465:Algorithms; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000209:etiology* / Q000503:physiopathology; D015716:Electrocardiography, Ambulatory / Q000379:methods* / Q000592:standards; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D007206:Individuality; D008297:Male; D011379:Prognosis; D012015:Reference Standards; D012119:Respiration; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software